class_docstr: Unittest for the gradients of the available acquisition functions
class_docstr_tok: ["Unittest","for","the","gradients","of","the","available","acquisition","functions"]
code: def setUp(self):
      np.random.seed(10)
      self.tolerance = 0.05  # Tolerance for difference between true and approximated gradients
      objective = GPyOpt.objective_examples.experiments1d.forrester()
      self.feasible_region = GPyOpt.Design_space(
          space=[
              {
                  "name": "var_1",
                  "type": "continuous",
                  "domain": objective.bounds[0],
              }
          ]
      )
      n_inital_design = 10
      X = samples_multidimensional_uniform(
          objective.bounds, n_inital_design
      )
      Y = objective.f(X)
      self.X_test = samples_multidimensional_uniform(
          objective.bounds, n_inital_design
      )
  
      self.model = Mock()
      self.model.get_fmin.return_value = 0.0
      self.model.predict_withGradients.return_value = (
          np.zeros(X.shape),
          np.zeros(Y.shape),
          np.zeros(X.shape),
          np.zeros(X.shape),
      )
  
file_path: GPyOpt/GPyOpt/testing/functional_tests/test_acquisitions_gradient.py
imports: import numpy
  import GPy
  from GPy.models.gradient_checker import GradientChecker
  import GPyOpt
  from GPyOpt.util.general import samples_multidimensional_uniform
  from GPyOpt.acquisitions import AcquisitionEI, AcquisitionMPI, AcquisitionLCB
  from mock import Mock
  import unittest
label_desc: Use image node_id 1 for calling the TestAcquisitionsGradients obj's underlying member method code with example usage: obj.setUp() without return types